Distribution of Database in Cloud Based on Associated Matrix

2014 ◽  
Vol 644-650 ◽  
pp. 3375-3378
Author(s):  
Lei Yue Yao ◽  
Wei Yang
Keyword(s):  
Response Time ◽  
Association Rules ◽  
Joint Distribution ◽  
Distributed Storage ◽  
Data Distribution ◽  
Data Manipulation ◽  
Distribution Strategy ◽  
Query Response Time ◽  
Cloud Database ◽  
Associated Matrix

Database is distributed stored in cloud. The paper found that joint distribution can base on frequent data manipulation, and frequent connection property. So it proposed cloud database data distribution strategy based on association rules. The data distributed storage is according to the frequent data manipulation rules. Experiments demonstrate that this strategy can significantly reduce database query response time, especially in connection query response time.

scholarly journals Re-optimization for Multi-objective Cloud Database Query Processing using Machine Learning

2021 ◽  
Vol 13 (1) ◽  
pp. 21-40
Author(s):  
Chenxiao Wang ◽  
Zach Arani ◽  
Le Gruenwald ◽  
Laurent d'Orazio ◽  
Eleazar Leal
Keyword(s):  
Machine Learning ◽  
Response Time ◽  
Cost Estimation ◽  
Service Providers ◽  
Database Management System ◽  
Query Execution ◽  
Execution Plan ◽  
Query Optimizer ◽  
Query Response Time ◽  
Cloud Database

In cloud environments, hardware configurations, data usage, and workload allocations are continuously changing. These changes make it difficult for the query optimizer of a cloud database management system (DBMS) to select an optimal query execution plan (QEP). In order to optimize a query with a more accurate cost estimation, performing query re-optimizations during the query execution has been proposed in the literature. However, some of there-optimizations may not provide any performance gain in terms of query response time or monetary costs, which are the two optimization objectives for cloud databases, and may also have negative impacts on the performance due to their overheads. This raises the question of how to determine when are-optimization is beneficial. In this paper, we present a technique called ReOptML that uses machine learning to enable effective re-optimizations. This technique executes a query in stages, employs a machine learning model to predict whether a query re-optimization is beneficial after a stage is executed, and invokes the query optimizer to perform the re-optimization automatically. The experiments comparing ReOptML with existing query re-optimization algorithms show that ReOptML improves query response time from 13% to 35% for skew data and from 13% to 21% for uniform data, and improves monetary cost paid to cloud service providers from 17% to 35% on skewdata.

Framework for GeoSpatial Query Processing by Integrating Cassandra With Hadoop

2019 ◽  
pp. 353-388
Author(s):  
S. Vasavi ◽  
Mallela Padma Priya ◽  
Anu A. Gokhale
Keyword(s):  
Response Time ◽  
Query Processing ◽  
Geospatial Data ◽  
Distributed Data ◽  
Nosql Databases ◽  
Storage And Retrieval ◽  
Query Response Time ◽  
Partitioning Algorithm ◽  
Hadoop Framework ◽  
Partitioning Technique

We are moving towards digitization and making all our devices, such as sensors and cameras, connected to internet, producing bigdata. This bigdata has variety of data and has paved the way to the emergence of NoSQL databases, like Cassandra, for achieving scalability and availability. Hadoop framework has been developed for storing and processing distributed data. In this chapter, the authors investigated the storage and retrieval of geospatial data by integrating Hadoop and Cassandra using prefix-based partitioning and Cassandra's default partitioning algorithm (i.e., Murmur3partitioner) techniques. Geohash value is generated, which acts as a partition key and also helps in effective search. Hence, the time taken for retrieving data is optimized. When users request spatial queries like finding nearest locations, searching in Cassandra database starts using both partitioning techniques. A comparison on query response time is made so as to verify which method is more effective. Results show the prefix-based partitioning technique is more efficient than Murmur3 partitioning technique.

Framework for GeoSpatial Query Processing by Integrating Cassandra With Hadoop

2018 ◽  
pp. 1-41
Author(s):  
S. Vasavi ◽  
Mallela Padma Priya ◽  
Anu A. Gokhale
Keyword(s):  
Response Time ◽  
Query Processing ◽  
Geospatial Data ◽  
Distributed Data ◽  
Nosql Databases ◽  
Storage And Retrieval ◽  
Query Response Time ◽  
Partitioning Algorithm ◽  
Hadoop Framework ◽  
Partitioning Technique

We are moving towards digitization and making all our devices, such as sensors and cameras, connected to internet, producing bigdata. This bigdata has variety of data and has paved the way to the emergence of NoSQL databases, like Cassandra, for achieving scalability and availability. Hadoop framework has been developed for storing and processing distributed data. In this chapter, the authors investigated the storage and retrieval of geospatial data by integrating Hadoop and Cassandra using prefix-based partitioning and Cassandra's default partitioning algorithm (i.e., Murmur3partitioner) techniques. Geohash value is generated, which acts as a partition key and also helps in effective search. Hence, the time taken for retrieving data is optimized. When users request spatial queries like finding nearest locations, searching in Cassandra database starts using both partitioning techniques. A comparison on query response time is made so as to verify which method is more effective. Results show the prefix-based partitioning technique is more efficient than Murmur3 partitioning technique.

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